Predicting Stock Prices Based on Price/Volume with Deep Learning and System Engineering Aggregate with Dynamic Behaviors and Trading Signals
Abstract
Johannes K. Chiang and Renhe Chi
Current stock market forecasting methods encompass fundamental, technical, emotional, and bargaining factors. Predominantly, price prediction hinges on order volume and price, although correlating these two within existing models proves challenging. This study employs Cycle Generative Adversarial Network (Cycle GAN) to unravel the intricate price-volume relationship, combining it with Bollinger Bands for trading signal analysis, overcoming hurdles in short-term forecasting prevalent in numerical analysis and AI.
Focusing on TSMC (2330.TW) stock price, the research leverages Cycle GAN in deep learning to master the price-volume nexus, juxtaposed with LSTM and RNN. Historical TSMC closing prices and transaction counts are model inputs, scrutinizing their interconnectedness for predictions. This innovative approach aligns stock price, volume, market value, taxes, and prior changes via system engineering. By intertwining Bollinger Bands with stock price forecasts, trading signals are distilled, factoring in extended index %b for a comprehensive market picture.
In this research, under the framework of simulation system engineering, the stock price forecasted using RESNET yielded a 20.3% return, which represents a significant increase when compared to the original Bollinger Bands average return on investment of 15.5%.